Support

A cookie is a piece of data stored by your browser or device that helps websites like this one recognize return visitors. We use cookies to give you the best experience on BNA.com. Some cookies are also necessary for the technical operation of our website. If you continue browsing, you agree to this site’s use of cookies.

Events

Bloomberg Next marketing services allow clients to elevate their brands and extend their reach through our established and trusted expertise, enhanced with engaging event production, appealing design, and compelling messaging.

Dynamic Scoring: Evidence From the States

Daily Tax Report: State provides authoritative coverage of state
and local tax developments across the 50 U.S. states and the District of
Columbia, tracking legislative and regulatory updates,...

Tax Policy

Dynamic scoring seeks to measure the economic effects of changes in policy. The Congressional
Budget Office uses dynamic scoring to estimate the effects of proposed tax legislation
on the federal budget. States also use dynamic scoring to predict the effects of state
tax policy changes. In this article, Georgia State University's Peter Bluestone discusses
the real-world experience states have had in using dynamic scoring for tax policy
making.

By Peter Bluestone

Peter Bluestone is a senior research associate with the Center for State and Local
Finance at Georgia State University. His research expertise includes urban economics,
static and dynamic economic impact modeling, and state and local fiscal policy. His
work has included modeling state and local impacts of policy changes and economic
development using various economic models, including IMPLAN and Regional Economic
Models Incorporated (REMI). He received his Ph.D. in economics and a J.D. from Georgia
State University.

Dynamic scoring is a hot topic again in Washington. Key members of the Trump administration,
including Treasury Secretary Steven Mnuchin, have stated that dynamic economic effects
will diminish the projected deficits of proposed changes to national tax policy. While
considerable scholarship exists modeling the effects at the national level of tax
policy changes, it is worthwhile to reflect on the experiences of states with dynamic
scoring. Not only do many states have considerable expertise working with dynamic
economic modeling and scoring, but a handful of states have recently implemented tax
cuts, with the stated belief that the resulting economic growth would offset part
of the projected budget deficits. The results from these real-world experiments provide
a cautionary tale to those who would rely on dynamic scoring to inform the budgetary
process at either the state or federal level.

Tax Cuts, Economic Growth

Recent experience in several states shows that merely cutting taxes does not automatically
result in sustained economic growth. Kansas, Louisiana, and Oklahoma all embraced
ambitious tax cuts phased in over several years. But instead of unprecedented economic
growth in the years that followed, these states found themselves facing budget deficits
and difficult choices: either reverse course on the tax cuts or limit government expenditures
in important areas such as education and health care.
(Louisiana and Oklahoma faced additional economic headwinds as the price of oil plunged
shortly after they announced their tax cuts.)
Eventually, the budget cutting became too painful, and all three states reversed course
and reinstated some of the previous tax policy and rates.

While not all of these states relied on dynamic scoring to justify their changes in
tax policy, the example of Kansas, which did have a dynamic analysis done, is illustrative
of the difficulties states face when trying to use dynamic scoring in the budgetary
process. Some background on what dynamic scoring is and how states use it is necessary
to illustrate these challenges.

State policymakers and their staffs in 21 states have experimented with dynamic scoring
since the early 1990s. While many states regularly use dynamic models to assess the
economic impact of infrastructure investments, almost all state-level efforts to dynamically
score tax policies for official budgetary purposes have been discontinued. The reasons
include unrealistic forecasts of revenue changes and the difficulty and expense of
harnessing a highly imprecise policy tool in a balanced-budget environment.

Economic Ripple Effects

Dynamic scoring is largely concerned with the economic ripple effects from a tax change.
Dynamic effects are often compared to the more traditional static revenue estimates,
which typically measure only the direct effects of a tax change, though selected behavioral
effects of tax changes (e.g., the effect of taxes on hours worked) may be incorporated.
A dynamic estimate, on the other hand, attempts to account for economic growth (or
decline)
associated with reduced (or increased) taxes. Thus, a tax cut that has an estimated
static effect of $100 million but that is dynamically scored might only reduce revenues
by an estimated $90 million —
a 10 percent dynamic effect.

In a joint
report by Georgia State University's Center for State and Local Finance and the Fiscal Research
Center, experts looked in detail at the range of dynamic effects in seven states that
reported the results of dynamic modeling. States reported dynamic effects ranging
from 1 to 20 percent of the static revenue estimate.

Despite the interest and some apparently large dynamic effects, almost all dynamic-scoring
efforts at the state level have been discontinued. The reasons are twofold: First,
even acknowledging economic-growth effects of tax cuts, the size of a dynamic effect
— even a large one —
is typically minuscule relative to the overall size of a state's budget. And second,
given the complexity of these estimates and timing issues
(when exactly does the dynamic effect occur?), the actual dynamic estimates are too
imprecise and too uncertain to be built into a state's budget in any meaningful way.

Experience in Kansas

Kansas' recent experiment with dynamic scoring illustrates these pitfalls. In 2012,
Kansas adopted major reductions in its income tax. For fiscal year 2015, the state
economist's static estimate was that revenues from the 2012 tax changes would decline
from $6.466 billion to $5.642 billion (an $824 million loss, or 13 percent). However,
a dynamic analysis from a pro-tax-cut research institute predicted that the state
would actually lose only
$714 million. That analysis forecasted that $110 million in additional revenues would
be recovered through economic growth, a dynamic effect of 13.5 percent (as measured
from the original static estimate of an $824 million loss).

The problem for the state was that even with such a large tax cut and a large estimated
dynamic effect, the estimated value of the dynamic effect would still be only 2 percent
of general-fund revenues. While dynamic models do not generate a margin-of-error estimate
for dynamic effects, research has shown that traditional revenue estimates carry an
error rate of around 3 percent.

Large Deficits, Massive Cuts

In the Kansas case, the state's revenues trended below the static revenue estimates,
causing large state budget deficits and massive cuts to state spending on education
and health care. Even several years after the implementation of the tax cuts, the
Kansas legislature in 2015 faced the daunting task of closing a $400 million budget
gap. By 2017, the Kansas legislature had seen enough; Kansas raised the top tax on
wage income and ended the special treatment of business income, despite a veto of
their earlier efforts by the governor.

Whether the dynamic effects from the Kansas tax cut failed to materialize, were incorrectly
estimated, or were simply lost in the normal error rate around a traditional revenue
estimate, is an open question. It is also a question that is likely to never be conclusively
resolved. Despite the hardship, Kansas was better served by sticking with the more
conservative static estimate —budgeting based on the dynamic estimate would have left
the state with even larger budget gaps to fill.

Comparing Economic Tradeoffs

None of this is to suggest that dynamic scoring can't be useful in comparing economic
tradeoffs among different tax-policy choices or even among different tax and expenditure
mixes. A recent
study done by Georgia State University's Fiscal Research Center modeled the effects on
the Georgia economy of fundamental tax reform in which a broad-based tax on consumption
(a sales tax) is substituted for the state's current personal income tax. The study
uses the dynamic state economic model from Regional Economic Models, Inc. (REMI),
to examine the effects of the tax reform on different sectors of the Georgia economy,
including labor force participation, savings and investments, consumer spending, and
industry sector employment, among others. The report finds that fundamental tax reform
has a modest effect on some of the areas studied but is dependent on the assumed changes
in the cost of capital in the state.

Distributional Effects

Another state that uses dynamic models to inform the policy debate but not as a budget
tool is Nebraska, which uses a dynamic model of the state economy to show the potential
impacts of various tax policy changes. In Nebraska, the state is particularly interested
in modeling how tax policy changes effect taxpayers across the income spectrum, known
as distributional effects. The experience of Georgia and Nebraska as well as other
states show that these models can be customized to highlight the distributional effects
of tax-policy changes across income classes or industry types.

Dynamic modeling has some interesting applications to policy analysis and provides
potentially useful information on the different ways that the effects of tax policies
ripple through the economy. For instance, noting that some tax changes may cause job
losses or declines in wages even while growing the productivity of the economy is
helpful information if a policymaker is largely concerned about job growth. Dynamic
models may also be quite useful in comparing tax and expenditure tradeoffs.

Forecast Uncertainty

Where dynamic modeling falls short, and what is apparently often disappointing to
policymakers, is that dynamic revenue analysis has not proved to be a particularly
useful tool for budgetary decision-making. A state's economy is a vastly complex system.
The results obtained from dynamic models rely heavily on assumptions made by the model
builders as well as on the availability of data. Even with the advances in computing
power and increased data availability, simplifying assumptions are needed, which increases
the uncertainty of any forecast.

Even assuming that the dynamic models are highly accurate, relatively large dynamic
effects, such as those estimated in Kansas, take time to materialize and are ultimately
small when compared to a state's general fund revenues. The practical effect is that
dynamic effects are likely to go unnoticed by the average citizen, state policymakers,
or state budget staff.

In light of these concerns, states contemplating the use of dynamic models should
consider several issues. First, what do policymakers want to learn from dynamic revenue
estimation? Based on recent state experiences, policymakers and analysts need to recognize
that dynamic revenue modeling can be useful for informing a policy debate, but policymakers
should generally not expect large effects and because of the uncertainty of the estimates
should avoid using these estimates in making budget decisions. Policymakers in states
such as Massachusetts in the 1990s and more recently, Kansas, found that the dynamic
effects take a long time to materialize. Second, states need to consider the resources
required to develop, customize and then interpret the results from a dynamic model.
These models are expensive to build and maintain and are complicated to use. More
than a few states have decided that the added value of the information is simply not
worth the money, time and effort required to purchase, develop, maintain, and use
dynamic models.

All Bloomberg BNA treatises are available on standing order, which ensures you will always receive the most current edition of the book or supplement of the title you have ordered from Bloomberg BNA’s book division. As soon as a new supplement or edition is published (usually annually) for a title you’ve previously purchased and requested to be placed on standing order, we’ll ship it to you to review for 30 days without any obligation. During this period, you can either (a) honor the invoice and receive a 5% discount (in addition to any other discounts you may qualify for) off the then-current price of the update, plus shipping and handling or (b) return the book(s), in which case, your invoice will be cancelled upon receipt of the book(s). Call us for a prepaid UPS label for your return. It’s as simple and easy as that. Most importantly, standing orders mean you will never have to worry about the timeliness of the information you’re relying on. And, you may discontinue standing orders at any time by contacting us at 1.800.960.1220 or by sending an email to books@bna.com.

Put me on standing order at a 5% discount off list price of all future updates, in addition to any other discounts I may quality for. (Returnable within 30 days.)

Notify me when updates are available (No standing order will be created).

This Bloomberg BNA report is available on standing order, which ensures you will all receive the latest edition. This report is updated annually and we will send you the latest edition once it has been published. By signing up for standing order you will never have to worry about the timeliness of the information you need. And, you may discontinue standing orders at any time by contacting us at 1.800.372.1033, option 5, or by sending us an email to research@bna.com.

Put me on standing order

Notify me when new releases are available (no standing order will be created)